Optimizing resource allocation to a bid request response based on cognitive analysis of natural language documentation

ABSTRACT

Approaches presented herein enable optimizing resource allocation to a bid request response based on a cognitive analysis of natural language artifacts. More specifically, a bid request and a plurality of supporting artifacts in a natural language are obtained. A cognitive analysis of the request and supporting artifacts is performed to extract a set of information entities. The extracted information entities are normalized using a lexical-relations based graph database to classify the set of extracted information entities as standardized concepts. A subset of the set of the standardized concepts is identified as a set of parameters corresponding to a set of predetermined variables. Each variable of the set of predetermined variables is weighted according to a likelihood that the variable indicates a relevance of a resource. A probability that a particular resource is relevant is determined based on the weighting and that resource is assigned to the bid request response.

TECHNICAL FIELD

The present invention relates generally to cognitive analysis ofdocuments in a natural language and, more specifically, to optimizingthe fulfillment of roles and responsibilities in a commercial bidrequest response based on a cognitive analysis of supporting documents.

BACKGROUND

For many organizations, working on a commercial bid request proposalposes several challenges. These challenges may include properlycomprehending a customer's terminology and keywords, which may beunfamiliar to the organization. As such, often within a short timeline,it becomes necessary for the organization to cross-reference andunderstand the customer's evaluation process to successfully interpretthe bid request and create an effective bid. Furthermore, it is oftennecessary to involve several consultants, specialists, and decisionmakers from across several departments in the process of creating a bidproposal. However, this may require several departments to collaborateand coordinate the analysis of the bid request as well as eachdepartment's contribution to the bid proposal.

SUMMARY

Approaches presented herein enable optimizing resource allocation to abid request response based on a cognitive analysis of natural languageartifacts. More specifically, a bid request and a plurality ofsupporting artifacts in a natural language are obtained. A cognitiveanalysis of the request and supporting artifacts is performed to extracta set of information entities. The extracted information entities arenormalized using a lexical-relations based graph database to classifythe set of extracted information entities as standardized concepts. Asubset of the set of the standardized concepts is identified as a set ofparameters corresponding to a set of predetermined variables. Eachvariable of the set of predetermined variables is weighted according toa likelihood that the variable indicates a relevance of a resource. Aprobability that a particular resource is relevant is determined basedon the weighting and that resource is assigned to the bid requestresponse.

One aspect of the present invention includes a method for optimizingresource allocation to a bid request response based on a cognitiveanalysis of natural language artifacts, comprising: obtaining a requestand a plurality of supporting artifacts in a natural language;performing a cognitive analysis of the request and supporting artifactsto extract a set of information entities; normalizing the extractedinformation entities using a lexical-relations based graph database toclassify the set of extracted information entities as standardizedconcepts with which the set of extracted information entities are mostclosely associated; identifying, for a portion of the request, at leasta subset of the set of the standardized concepts, with which the set ofextracted information entities are most closely associated, as a set ofparameters corresponding with a set of predetermined variables;weighting each variable of the set of predetermined variables accordingto a likelihood that the variable indicates a relevance of a resource tothe portion of the request; and assigning a particular resource to thebid request response in response to a probability that the particularresource is relevant to the portion of the request based on the weightedvariables.

Another aspect of the present invention includes a computer system foroptimizing resource allocation to a bid request response based on acognitive analysis of natural language artifacts, the computer systemcomprising: a memory medium comprising program instructions; a buscoupled to the memory medium; and a processor, for executing the programinstructions, coupled to a bid request analysis engine via the bus thatwhen executing the program instructions causes the system to: obtain arequest and a plurality of supporting artifacts in a natural language;perform a cognitive analysis of the request and supporting artifacts toextract a set of information entities; normalize the extractedinformation entities using a lexical-relations based graph database toclassify the set of extracted information entities as standardizedconcepts with which the set of extracted information entities are mostclosely associated; identify, for a portion of the request, at least asubset of the set of the standardized concepts, with which the set ofextracted information entities are most closely associated, as a set ofparameters corresponding with a set of predetermined variables; weighteach variable of the set of predetermined variables according to alikelihood that the variable indicates a relevance of a resource to theportion of the request; and assign a particular resource to the bidrequest response in response to a probability that the particularresource is relevant to the portion of the request based on the weightedvariables.

Yet another aspect of the present invention includes a computer programproduct for optimizing resource allocation to a bid request responsebased on a cognitive analysis of natural language artifacts, thecomputer program product comprising a computer readable hardware storagedevice, and program instructions stored on the computer readablehardware storage device, to: obtain a request and a plurality ofsupporting artifacts in a natural language; perform a cognitive analysisof the request and supporting artifacts to extract a set of informationentities; normalize the extracted information entities using alexical-relations based graph database to classify the set of extractedinformation entities as standardized concepts with which the set ofextracted information entities are most closely associated; identify,for a portion of the request, at least a subset of the set of thestandardized concepts, with which the set of extracted informationentities are most closely associated, as a set of parameterscorresponding with a set of predetermined variables; weight eachvariable of the set of predetermined variables according to a likelihoodthat the variable indicates a relevance of a resource to the portion ofthe request; and assign a particular resource to the bid requestresponse in response to a probability that the particular resource isrelevant to the portion of the request based on the weighted variables.

Still yet, any of the components of the present invention could bedeployed, managed, serviced, etc., by a service provider who offers toimplement passive monitoring in a computer system.

Embodiments of the present invention also provide related systems,methods, and/or program products.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other features of this invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings in which:

FIG. 1 shows an architecture in which the invention may be implementedaccording to illustrative embodiments.

FIG. 2 shows a system diagram describing the functionality discussedherein according to illustrative embodiments.

FIG. 3 shows analysis of a commercial bid request to optimize a list ofresource roles to generate a response to the request according toillustrative embodiments.

FIG. 4 shows a process flowchart for optimizing resource allocation to abid request response based on a cognitive analysis of natural languageartifacts according to illustrative embodiments.

The drawings are not necessarily to scale. The drawings are merelyrepresentations, not intended to portray specific parameters of theinvention. The drawings are intended to depict only typical embodimentsof the invention, and therefore should not be considered as limiting inscope. In the drawings, like numbering represents like elements.

DETAILED DESCRIPTION

Illustrative embodiments will now be described more fully herein withreference to the accompanying drawings, in which illustrativeembodiments are shown. It will be appreciated that this disclosure maybe embodied in many different forms and should not be construed aslimited to the illustrative embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this disclosure to thoseskilled in the art.

Furthermore, the terminology used herein is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of this disclosure. As used herein, the singular forms “a”,“an”, and “the” are intended to include the plural forms as well, unlessthe context clearly indicates otherwise. Furthermore, the use of theterms “a”, “an”, etc., do not denote a limitation of quantity, butrather denote the presence of at least one of the referenced items.Furthermore, similar elements in different figures may be assignedsimilar element numbers. It will be further understood that the terms“comprises” and/or “comprising”, or “includes” and/or “including”, whenused in this specification, specify the presence of stated features,regions, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,regions, integers, steps, operations, elements, components, and/orgroups thereof.

Unless specifically stated otherwise, it may be appreciated that termssuch as “processing,” “detecting,” “determining,” “evaluating,”“receiving,” or the like, refer to the action and/or processes of acomputer or computing system, or similar electronic data center device,that manipulates and/or transforms data represented as physicalquantities (e.g., electronic) within the computing system's registersand/or memories into other data similarly represented as physicalquantities within the computing system's memories, registers or othersuch information storage, transmission or viewing devices. Theembodiments are not limited in this context.

As stated above, embodiments described herein provide for optimizingresource allocation to a bid request response based on a cognitiveanalysis of natural language artifacts. More specifically, a bid requestand a plurality of supporting artifacts in a natural language areobtained. A cognitive analysis of the request and supporting artifactsis performed to extract a set of information entities. The extractedinformation entities are normalized using a lexical-relations basedgraph database to classify the set of extracted information entities asstandardized concepts. A subset of the set of the standardized conceptsis identified as a set of parameters corresponding to a set ofpredetermined variables. Each variable of the set of predeterminedvariables is weighted according to a likelihood that the variableindicates a relevance of a resource. A probability that a particularresource is relevant is determined based on the weighting and thatresource is assigned to the bid request response.

Referring now to FIG. 1, a computerized implementation 10 of anembodiment for optimizing resource allocation to a bid request responsebased on a cognitive analysis of natural language artifacts will beshown and described. Computerized implementation 10 is only one exampleof a suitable implementation and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, computerized implementation 10is capable of being implemented and/or performing any of thefunctionality set forth hereinabove.

In computerized implementation 10, there is a computer system/server 12,which is operational with numerous other general purpose or specialpurpose computing system environments or configurations. Examples ofwell-known computing systems, environments, and/or configurations thatmay be suitable for use with computer system/server 12 include, but arenot limited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

This is intended to demonstrate, among other things, that the presentinvention could be implemented within a network environment (e.g., theInternet, a wide area network (WAN), a local area network (LAN), avirtual private network (VPN), etc.), a cloud computing environment, acellular network, or on a stand-alone computer system. Communicationthroughout the network can occur via any combination of various types ofcommunication links. For example, the communication links can compriseaddressable connections that may utilize any combination of wired and/orwireless transmission methods. Where communications occur via theInternet, connectivity could be provided by conventional TCP/IPsockets-based protocol, and an Internet service provider could be usedto establish connectivity to the Internet. Still yet, computersystem/server 12 is intended to demonstrate that some or all of thecomponents of implementation 10 could be deployed, managed, serviced,etc., by a service provider who offers to implement, deploy, and/orperform the functions of the present invention for others.

Computer system/server 12 is intended to represent any type of computersystem that may be implemented in deploying/realizing the teachingsrecited herein. Computer system/server 12 may be described in thegeneral context of computer system/server executable instructions, suchas program modules, being executed by a computer system. Generally,program modules may include routines, programs, objects, components,logic, data structures, and so on, that perform particular tasks orimplement particular abstract data types. In this particular example,computer system/server 12 represents an illustrative system foroptimizing resource allocation to a bid request response based on acognitive analysis of natural language artifacts. It should beunderstood that any other computers implemented under the presentinvention may have different components/software, but can performsimilar functions.

Computer system/server 12 in computerized implementation 10 is shown inthe form of a general-purpose computing device. The components ofcomputer system/server 12 may include, but are not limited to, one ormore processors or processing units 16, a system memory 28, and a bus 18that couples various system components including system memory 28 toprocessing unit 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Processing unit 16 refers, generally, to any apparatus that performslogic operations, computational tasks, control functions, etc. Aprocessor may include one or more subsystems, components, and/or otherprocessors. A processor will typically include various logic componentsthat operate using a clock signal to latch data, advance logic states,synchronize computations and logic operations, and/or provide othertiming functions. During operation, processing unit 16 collects androutes signals representing inputs and outputs between external devices14 and input devices (not shown). The signals can be transmitted over aLAN and/or a WAN (e.g., T1, T3, 56 kb, X.25), broadband connections(ISDN, Frame Relay, ATM), wireless links (802.11, Bluetooth, etc.), andso on. In some embodiments, the signals may be encrypted using, forexample, trusted key-pair encryption. Different systems may transmitinformation using different communication pathways, such as Ethernet orwireless networks, direct serial or parallel connections, USB,Firewire®, Bluetooth®, or other proprietary interfaces. (Firewire is aregistered trademark of Apple Computer, Inc. Bluetooth is a registeredtrademark of Bluetooth Special Interest Group (SIG)).

In general, processing unit 16 executes computer program code, such asprogram code for optimizing resource allocation to a bid requestresponse based on a cognitive analysis of natural language artifacts,which is stored in memory 28, storage system 34, and/or program/utility40. While executing computer program code, processing unit 16 can readand/or write data to/from memory 28, storage system 34, andprogram/utility 40.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia, (e.g., VCRs, DVRs, RAID arrays, USB hard drives, optical diskrecorders, flash storage devices, and/or any other data processing andstorage elements for storing and/or processing data). By way of exampleonly, storage system 34 can be provided for reading from and writing toa non-removable, non-volatile magnetic media (not shown and typicallycalled a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM, or other optical media can be provided. In such instances, eachcan be connected to bus 18 by one or more data media interfaces. As willbe further depicted and described below, memory 28 may include at leastone program product having a set (e.g., at least one) of program modulesthat are configured to carry out the functions of embodiments of theinvention.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium including, but not limited to, wireless,wireline, optical fiber cable, radio-frequency (RF), etc., or anysuitable combination of the foregoing.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation. Memory28 may also have an operating system, one or more application programs,other program modules, and program data. Each of the operating system,one or more application programs, other program modules, and programdata or some combination thereof, may include an implementation of anetworking environment. Program modules 42 generally carry out thefunctions and/or methodologies of embodiments of the invention asdescribed herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a consumer to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via I/O interfaces22. Still yet, computer system/server 12 can communicate with one ormore networks such as a local area network (LAN), a general wide areanetwork (WAN), and/or a public network (e.g., the Internet) via networkadapter 20. As depicted, network adapter 20 communicates with the othercomponents of computer system/server 12 via bus 18. It should beunderstood that although not shown, other hardware and/or softwarecomponents could be used in conjunction with computer system/server 12.Examples include, but are not limited to: microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

The inventors of the present invention have found that, for manyorganizations, working on a proposal responsive to a commercial bidrequest poses several challenges, such as using a customer's terminologyand keywords, understanding the customer's evaluation process, andinterpreting the customer's commercial bid request within a typicallyvery short timeline. Often, this process can require reviewing at leasthundreds of pages of documentation, while ensuring that all resourcesacross several departments seamlessly function together. It is commonfor several consultants, specialists and decision makers to be involvedin the bid proposal process, rather than simply one department orindividual. Therefore, responding to a bid request may require multipledepartments to analyze, collaborate, and contribute during theproposal/bid development process. Furthermore, with no industrystandardization for such proposals and with varying and unique formatsand components for each solution, it can be difficult to produce aneffective bid proposal in a time-bound manner. For example, assessingand prioritizing the needs of the organization and the customer, such aswhich features and capabilities should be prioritized for each “ask” ofthe bid request, can be a daunting and infeasible task for a person orgroup of persons.

Moreover, present processes and tools for responding to a request for acommercial bid proposal have several limitations. These limitationsinclude the inability to identify relevant roles within the organizationbased on industry or domain taxonomy and contextual factors, therebyfailing to ensure consistent and comprehensive participation andcollaboration by all necessary departments or stakeholders. Theselimitations further include the necessity of relying on manual andcumbersome analysis to align the right people for developing the bidproposal response. Such manual analysis can be highly inefficient andineffective because a human analyzer does not have the time or abilityto review all potentially relevant documentation (which can number inthe hundreds or thousands of pages) or assess how each department orstakeholder may fit within the context of the bid proposal response inthe short time permitted between when a bid request is received and whena response is due.

Accordingly, the inventors of the present invention have developed asystem that uses entity-relationship analysis and deep learning methodsto analyze specialization dimensions in commercial bid documents withefficiency. Embodiments of the present invention further leveragegraphical database concepts in combination with supervised learningtechniques that use contextual parameters related to, among otherthings, an industry or domain, a client, a nature of a deal, and anidentity of a bidder organization. Based on this analysis, adetermination of a set of roles that need to be involved in a commercialbid proposal is enabled.

Furthermore, embodiments of the present invention offer severaladvantages for optimizing resource allocation to a bid request responsebased on a cognitive analysis of natural language artifacts. Forexample, embodiments of the present invention enable the identificationof roles relevant to each document section of a commercial bid proposalrequest at both an atomic and an aggregate level based on industry ordomain taxonomy and contextual factors. This ensures consistent andcomprehensive participation and collaboration by all necessarydepartments or stakeholders in drafting a commercial bid. Furthermore,embodiments of the present invention enable processing oftechno-functional bid documents as well as supporting artifacts with anefficiency and capability exceeding that of a human being, permitting athorough analysis to be performed on these documents within the shortconfines of a commercial bid window.

Referring now to FIG. 2, a system diagram describing the functionalitydiscussed herein according to an embodiment of the present invention isshown. It is understood that the teachings recited herein may bepracticed within any type of computing environment, including, but notlimited to, a networked computing environment (e.g., a cloud computingenvironment). A stand-alone computer system/server 12 is shown in FIG. 2for illustrative purposes only. In the event the teachings recitedherein are practiced in a networked computing environment, each clientneed not have a bid request analysis engine 200 (hereinafter “system200”). Rather, all or part of system 200 could be loaded on a server orserver-capable device that communicates (e.g., wirelessly) with theclients to provide for optimizing resource allocation to a bid requestresponse based on a cognitive analysis of natural language artifacts.Regardless, as depicted, system 200 is shown within computersystem/server 12. In general, system 200 can be implemented asprogram/utility 40 on computer system 12 of FIG. 1 and can enable thefunctions recited herein.

Along these lines, system 200 may perform multiple functions similar toa general-purpose computer. Specifically, among other functions, system200 can optimize resource allocation to a bid request response based ona cognitive analysis of natural language artifacts in a networkedcomputing environment. To accomplish this, system 200 can include a setof components (e.g., program modules 42 of FIG. 1) for carrying outembodiments of the present invention. These components can include, butare not limited to, bid request and artifact obtainer 202, cognitiveextractor 204, cognitive contextual classifier 206, parameter extractor208, variable analyzer 210, and role identifier 212.

Through computer system/server 12, system 200 can receive/obtaincommercial bid request 220 in a natural language and/or supportingartifacts 230 (e.g., documentation), associated with commercial bidrequest 220, also in a natural language. Such supporting artifacts 230may include, for example, historical data 232A (e.g., copies of previoussuccessful/unsuccessful bid proposals), public information 232B (e.g.,press releases), and/or correspondence 232C (e.g., letters, emails,etc.).

In some embodiments, system 200 can have access to industry and domainconcepts data 240 and content classification taxonomy data 250. In someembodiments, this information or data can be stored within storagesystem 34 of computer system/server 12. In still other embodiments, thisinformation or data can be obtained from a source outside of computersystem/server 12, such as an external database. In still otherembodiments, industry and domain concepts data 240 and contentclassification taxonomy data 250 can be part of a component of system200, such as cognitive contextual classifier 204.

Through computer system/server 12, system 200 can also receive/obtaininformation about an organization's resources, roles, and/or personnel(e.g., departments, individuals, other stakeholders), such as in theform of resource organizational hierarchy 260. Resource organizationalhierarchy 260 may include, for example, but is not limited to, a tieredlist of departments of the organization, a tiered list of roles withinthe organization, a tiered list of personnel within the organization,etc.

Referring now to FIG. 3 in connection with FIG. 2, analyzing commercialbid request 220 to optimize a list of resource roles to generate aresponse to request for a commercial bid 220 according to illustrativeembodiments is shown. Bid request and artifact obtainer 202 of system200, as performed by computer system/server 12, can obtain or otherwisereceive commercial request for a commercial bid 220 and a plurality ofsupporting artifacts 230 in a natural language. According to embodimentsof the present invention, obtainer 202 can receive a bid request anynumber of ways. In some embodiments, commercial bid proposal request 220can be fed to system 200 manually by a user/person or automatically byanother system (e.g., in response to a push or pull command). In someother embodiments, obtainer 202 can be configured to seek an incomingcommercial bid request 220 being transmitted to an organization (e.g.,as an email attachment) or to search for a commercial bid request 220from a remote source (e.g., as a posting on a website).

In some embodiments, commercial bid request 220 can be a document in anatural language. A natural language is any language that developsnaturally for communication between people. This commercial bid request220 can be a request for a bidder organization (e.g., a commercialcompany) associated with system 200 to generate a bid or proposal for abid-seeking/requesting organization (e.g., a commercial company, agovernment entity) from which request 220 originates. Although there isno industry standard for the composition of a commercial bid request, itshould be understood that, according to embodiments of the presentinvention, commercial bid request 220 may include many sections,detailing “asks”, needs, or other requirements of the bid-seekingorganization. Commercial bid request 220 may number in the hundreds oreven thousands of pages in length. As such, in embodiments of thepresent invention, commercial bid request 220 can be a natural languagedocument of sufficient complexity and length as to make human review andcomprehension of request 220 impractical in a time window provided byrequest 220.

In addition to commercial bid request 220, bid request and artifactobtainer 202 can obtain a plurality of supporting artifacts 230 in anatural language. In some embodiments, supporting artifacts 230 caninclude, but are not limited to and need not include, historical data232A (e.g., copies of previous successful/unsuccessful bid proposals),publicly available information 232B (e.g., press releases), and/orcorrespondence 232C (e.g., letters, emails, etc.). Supporting artifacts230 can include any document or other media that may be of assistance inoptimizing resource allocation to create a bid responsive to commercialbid request 220. Furthermore, supporting artifacts 230 may be, but neednot be, in the same natural language as request 220. In someembodiments, supporting artifacts 230 can be textual documents in anatural language, but are not limited to such. Supporting artifacts 230can also or alternatively be other media in a natural language, such assound recordings, videos, and images.

Bid request and artifact obtainer 202 can obtain supporting artifacts230 from any source and is not limited to obtaining supporting artifacts230 from the same source from which obtainer 202 obtained commercial bidrequest 220. In some embodiments, supporting artifacts 230 can be fed tosystem 200 manually by a user/person or automatically by another system(e.g., in response to a push or pull command). In some otherembodiments, obtainer 202 can be configured to seek supporting artifacts230. To seek supporting artifacts 230, obtainer 202 can, for example,search one or more datastores (e.g., storage system 34 of computersystem/server 12 or any other system) or cause such datastores to besearched. For instance, if historical data 232A (e.g., previous deals)or correspondence 232C is stored by a bidder organization, obtainer 202can review that stored data for pertinent information. To seeksupporting artifacts 230, obtainer 202 can also perform an internetsearch or cause an internet search to be performed to discoverinformation about the bid-seeking organization. Obtainer 202 can use aninternet search to find public information 232B, such as press releases,articles about the bid-seeking organization, and information that thebid-seeking organization shares with the public, such as on thebid-seeking organization's website.

In some embodiments, bid request and artifact obtainer 202 can beconfigured to generate, based on the commercial bid request 220, searchqueries for searching both local and remote sources of information. Forexample, based on request 220, obtainer 202 can generate a query thatsearches for the bid-seeking organization's name in connection to atechnological area that request 220 regards. In some embodiments, bidrequest and artifact obtainer 202 can perform this function subsequentto cognitive extractor 204 (discussed below) extracting documentinsights 370 from request 220. As such, obtainer 202 can be configuredto search for supporting artifacts 230 based on insights 370 extractedfrom request 220.

Cognitive extractor 204 of system 200, as performed by computersystem/server 12, can initiate the performance of a cognitive analysisof commercial bid request 220 and/or supporting artifacts 230 byextracting document insights 370 and information entities from request220 and/or artifacts 230 at cognitive extraction/enrichment 302. As usedhere, an entity can be derived from different parameters (discussed inmore detail below), while a co-relation between different parameters canbe mapped to a single entity. According to embodiments of the presentinvention, cognitive extractor 204 can include or be in communicationwith a cognitive analysis system or other artificial neural networksystem similar to IBM's Watson that uses, for example, IBM's DeepQAsoftware within an Apache UIMA (Unstructured Information ManagementArchitecture) framework. All trademarks and trade names used herein arethe property of their respective owners and are used for illustrativeand descriptive purposes only. As part of this cognitive analysisprocess, cognitive extractor 204 can apply industry domain conceptsand/or entity relationship corpus 240 to request 220 and/or artifacts230 to co-relate metadata and information entities contained in request220 and/or artifacts 230 to produce document insights 370. Such documentinsights can include, but are not limited to, a context hierarchy and/ora topical classification of the information entities contained inrequest 220 and/or artifacts 230. According to embodiments of thepresent invention, this cognitive analysis process can be performed by acognitive system that has derived a corpus specific to a particulardomain based on multiple datasets, establishing a data lake derived frommultiple data repositories across a specific domain. Cognitive extractor204 can use a machine learning discovery algorithm to extractinformation from request 220 and/or artifacts 230. Cognitive extractor204 can also or alternatively use cognitive text analytics to map andsegment metadata associated with request 220 and/or artifacts 230 to aspecific domain. Accordingly, cognitive extractor 204 can index andextract keywords or entities from request 220 based on context,permitting evolution of a self-learning performance model that useshistorical insights.

Cognitive contextual classifier 206 of system 200, as performed bycomputer system/server 12, can classify document insights 370 and otherinformation entities within content classification taxonomy 250 todetermine relationships between various document insights 370 and otherinformation entities at 304. According to some embodiments of thepresent invention, cognitive contextual classifier 206 can employ ascoring method (e.g., a confidence level) to determine relatedbehavioral outliers (i.e., to provide greater accuracy in specificmodels) based on extracted document insights 370 associated with eachspecialization dimension of commercial bid request 220. A dimension ofcommercial bid request 220 is defined as a construct whereby objects orindividuals can be distinguished, such as, for example, time, location,confidence level, connectivity management infrastructure, etc. Usingthis scoring method, cognitive contextual classifier 206 can assign aweighted sum to an information entity or document insight 370. Thisweighted sum can be configured to decrease in proportion to a traverseddistance from an extracted concept node to the insight/entity. Cognitivecontextual classifier 206 can use this weighted sum as an index, where avalue higher than a predetermined threshold suggests that entity/insight370 may be more relevant in the context (i.e., to entities which arederived from different datasets having multiple features) and to thedimension. In some embodiments, cognitive contextual classifier 206 canleverage a lexical-relations based graphical database (not shown) tonormalize the concepts and entities extracted from request 220 and/orartifacts 230 to determine standardized concepts and entities with whichthe extracted concepts and entities are most closely associated.Concepts and entities extracted from request 220 and/or artifacts 230are classified based on different dimensions of the document from whichthey originated. This classification process permits classificationtaxonomy 250 to provide context for a specific set of content.

Parameter extractor 208 of system 200, as performed by computersystem/server 12, can, for each section or other subdivision of request220 for a commercial bid, find and extract a set of parameters 380corresponding with a dimension of request 220 from the standardizedconcepts and entities of classified document insights and informationentities 370. Recognition of parameters 380 can be based on the taxonomyclassification and relationship analysis performed by cognitivecontextual classifier 206 of insights/entities 370 in a natural languagein request 220 and supporting artifacts insights/entities 370 forparameters 380 for each section of request 220 across various dimensionsof request 220. In some embodiments, parameters 380 can correspond toone or more variables that can include, but are not limited to, any ofthe variables listed in the table below. For example, a parameter ofparameters 380 can indicate an industry/domain, a client, a nature of adeal, the bid-seeking organization, etc.

Variable analyzer 210 of system 200, as performed by computersystem/server 12, can identify a set of predictor variables most likelyto indicate a role or stakeholder most suited to contribute todevelopment of a bid responsive to a section or other division ofrequest 220 for a commercial bid based on extracted parameters 380. Toaccomplish this, variable analyzer 210 can use a self-learning system(previously trained with learning data) to identify input variables thatindividually or in combination help identify the most suited roles orstakeholders based on context and relationships between various dealrelated parameters, domain considerations, organization-specificfactors, etc. For example, variable analyzer 210 can identify parameters380 as corresponding to any pre-defined variable such as, but notlimited to, the following shown in the table below:

PREDICTOR PARAMETER/ PARAMETER VARIABLE AREA ENTITY DESCRIPTION  x1 DealRelated Request Type For example, whether a request for information,technical proposal, budgetary estimate, commercial proposal, etc.  x2Deal Size  x3 Techno-Functional Techno-functional area Area ofinvestment  x4 Deal Type For example, whether greenfield, brownfield,etc.  x5 Deal Coverage Project phases to be bid for, for example,consulting, design, implementation, maintenance etc.  x6 Domain orIndustry  x7 Industry Sub-domain  x8 Related Techno-FunctionalTechno-functional area Area of investment  x9 Geography Socio-regionalfactors such as local regulations, taxation, etc. x10 Client Client Sizex11 Related Historical Spending Average historical spending on similardeals x12 Inclination to Historical inclination Bidder to bidder interms of past contracts, etc. x13 Inclination to Composite or individualPartners scores indicating inclination to, for example, hardware,software, consulting, support, ancillary products & services partners,vendors, etc. x14 Bidder Bidder Strength in Organization Domain x15Related Sales Organization Size x16 Document Section or Sub- Sectionsection Topic x17 Related Techno-Functional Techno-functional Entitiesentities and related entities for the section

Role identifier 212 of system 200, as performed by computersystem/server 12, can identify, based on the parameters of theidentified variable, a role 390 to be involved with the development of asection of a bid responsive to a section or other division of request220 for a commercial bid at 306. To accomplish this, for each section,role identifier 212 uses a first-order supervised multi-label learningclassification algorithm in a self-learning system (previously trainedwith learning data) to identify one or more roles 390, based onparameters 380 corresponding with the identified variables, which have aprobability of being needed to be assigned for one or more dimensions ofthe bid/proposal development process. In some embodiments, thisalgorithm can be a model that predicts role 390 required for eachdimension/specialization, as a set of binary labels whose values arepredicted based on the input predictor variables identified by variableanalyzer 210.

For example, in some embodiments, role identifier 212 can use aMulti-Label k-Nearest Neighbor (ML-kNN) model, which is a first-orderlearning algorithm that reasons the relevance of each outputindependently of other possible outputs, without attempting to establishcorrelations between the possible outputs. This permits the k-nearestneighbor model to process discrete labels or output classes using amaximum a posteriori (MAP) rule to make a prediction by reasoning withthe labeling information embodied in the neighbors. Role identifier 212can use this technique to weigh the identified variables based on theirdetermined likelihood to indicate role or stakeholder 390 is suited tocontribute to development of a commercial bid, and from this weighting,determine a probability that role 390 is relevant to a particulardimension of request 220, for a given value, range, or other parameter380 corresponding to the variables.

In some embodiments, parameter extractor 208, variable analyzer 210, androle identifier 212 can iterate their above discussed functions severaltimes until, for each section of request 220, a variance of theoutputted probability that a role 390 is relevant is below a pre-definedthreshold. Furthermore, each iteration permits enhancement of theinfluence of the identified predictor variables on each output label, byanalyzing the influence across all the predictor variables acrossvarious sections of bid request 220. In other words, in cases wherethere are several predictor variables, some predictor variables have astronger influence on output, indicated by a higher confidence score forthat variable. As will be discussed in more detail below, theseidentified influencer predictor variables are used to generate aconfidence level that a role is relevant for each of a set of identifiedroles.

Role identifier 212 ultimately outputs identification of a set of roles390 that are relevant to a particular dimension of request 220 for acommercial bid. Role identifier 212 bases this output on the parameterscorresponding to the variables determined during the weighting to bemost likely to be indicative of a role's relevance to the commercialbid. In some embodiments, role identifier 212 assigns a set of roles orresources 390 to respond to request 220 for a commercial bid. In oneembodiment, role identifier 212 assigns roles 390 with an associatedrelevance probability over a predetermined threshold probability torespond to request 220. In some embodiments, role identifier 212 canmatch identified roles 390 to specific personnel within the bidderorganization. In still further embodiments, role identifier 212 canassign specific personnel to a group that will respond to request 220based on a match between a role of identified roles 390 and the person.

Role identifier 212 can express the probabilities that a set of roles390 are relevant to a particular dimension of request 220 for acommercial bid in any format representative of a probability, such as apercentage (e.g., X % out of 100%) or a fraction (e.g., X1/X2), but arenot limited to such. In some embodiments, role identifier 212 can outputset of roles 390 as listing 290 of roles ordered or ranked according toa probability that a particular role 390 is relevant to the developmentof a bid proposal responsive to request 220 for a commercial bid. Therole probability output of role identifier 212 need not be rankedaccording to probability. For example, in some embodiments, roles 390and their associated probabilities of relevance can be expressed in analternative order (e.g., alphabetically) or even randomly. In someembodiment, the role probability output of role identifier 212 can beexpressed as a truncated list, in which only roles 390 with anassociated relevance probability over a predetermined thresholdprobability are outputted. In still other embodiments, role identifier212 can be configured to output a probability that a particular role oreach of a particular set of roles is relevant to request 220.

As depicted in FIG. 4, in one embodiment, a system (e.g., computersystem/server 12) carries out the methodologies disclosed herein. Shownis a process flowchart 400 for optimizing resource allocation to a bidrequest response based on a cognitive analysis of natural languageartifacts. At 402, bid request and artifact obtainer 202 obtains arequest 220 and a plurality of supporting artifacts 230 in a naturallanguage. At 404, cognitive extractor 204 performs a cognitive analysis302 of the request 220 and supporting artifacts 230 to extract a set ofinformation entities 370. At 406, cognitive contextual classifier 206normalizes the extracted information entities 370 using alexical-relations based graph database 250 to classify the set ofextracted information entities 370 as standardized concepts with whichthe set of extracted information entities 370 are most closelyassociated. At 408, parameter extractor 208 identifies, for a portion ofthe request 220, at least a subset of the set of the standardizedconcepts, with which the set of extracted information entities 370 aremost closely associated, as a set of parameters 380 corresponding with aset of predetermined variables. At 410, variable analyzer 210, weightseach variable of the set of predetermined variables according to alikelihood that the variable indicates a relevance of a resource 390 tothe portion of the request 220. At 412, role identifier 212 assigns aparticular resource 390 to the bid request response in response to aprobability that the particular resource 390 is relevant to the portionof the request 220 based on the weighted variables.

Process flowchart 400 of FIG. 4 illustrates the architecture,functionality, and operation of possible implementations of systems,methods, and computer program products according to various embodimentsof the present invention. In this regard, each block in the flowchart orblock diagrams may represent a module, segment, or portion ofinstructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Some of the functional components described in this specification havebeen labeled as systems or units in order to more particularly emphasizetheir implementation independence. For example, a system or unit may beimplemented as a hardware circuit comprising custom VLSI circuits orgate arrays, off-the-shelf semiconductors such as logic chips,transistors, or other discrete components. A system or unit may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices, orthe like. A system or unit may also be implemented in software forexecution by various types of processors. A system or unit or componentof executable code may, for instance, comprise one or more physical orlogical blocks of computer instructions, which may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified system or unit need not be physicallylocated together, but may comprise disparate instructions stored indifferent locations which, when joined logically together, comprise thesystem or unit and achieve the stated purpose for the system or unit.

Further, a system or unit of executable code could be a singleinstruction, or many instructions, and may even be distributed overseveral different code segments, among different programs, and acrossseveral memory devices. Similarly, operational data may be identifiedand illustrated herein within modules, and may be embodied in anysuitable form and organized within any suitable type of data structure.The operational data may be collected as a single data set, or may bedistributed over different locations including over different storagedevices and disparate memory devices.

Furthermore, systems/units may also be implemented as a combination ofsoftware and one or more hardware devices. For instance, program/utility40 may be embodied in the combination of a software executable codestored on a memory medium (e.g., memory storage device). In a furtherexample, a system or unit may be the combination of a processor thatoperates on a set of operational data.

As noted above, some of the embodiments may be embodied in hardware. Thehardware may be referenced as a hardware element. In general, a hardwareelement may refer to any hardware structures arranged to perform certainoperations. In one embodiment, for example, the hardware elements mayinclude any analog or digital electrical or electronic elementsfabricated on a substrate. The fabrication may be performed usingsilicon-based integrated circuit (IC) techniques, such as complementarymetal oxide semiconductor (CMOS), bipolar, and bipolar CMOS (BiCMOS)techniques, for example. Examples of hardware elements may includeprocessors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor devices, chips,microchips, chip sets, and so forth. However, the embodiments are notlimited in this context.

Any of the components provided herein can be deployed, managed,serviced, etc., by a service provider that offers to deploy or integratecomputing infrastructure with respect to a process for optimizingresource allocation to a bid request response based on a cognitiveanalysis of natural language artifacts. Thus, embodiments hereindisclose a process for supporting computer infrastructure, comprisingintegrating, hosting, maintaining, and deploying computer-readable codeinto a computing system (e.g., computer system/server 12), wherein thecode in combination with the computing system is capable of performingthe functions described herein.

In another embodiment, the invention provides a method that performs theprocess steps of the invention on a subscription, advertising, and/orfee basis. That is, a service provider, such as a Solution Integrator,can offer to create, maintain, support, etc., a process for optimizingresource allocation to a bid request response based on a cognitiveanalysis of natural language artifacts. In this case, the serviceprovider can create, maintain, support, etc., a computer infrastructurethat performs the process steps of the invention for one or morecustomers. In return, the service provider can receive payment from thecustomer(s) under a subscription and/or fee agreement, and/or theservice provider can receive payment from the sale of advertisingcontent to one or more third parties.

Also noted above, some embodiments may be embodied in software. Thesoftware may be referenced as a software element. In general, a softwareelement may refer to any software structures arranged to perform certainoperations. In one embodiment, for example, the software elements mayinclude program instructions and/or data adapted for execution by ahardware element, such as a processor. Program instructions may includean organized list of commands comprising words, values, or symbolsarranged in a predetermined syntax that, when executed, may cause aprocessor to perform a corresponding set of operations.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is apparent that there has been provided herein approaches tooptimize resource allocation to a bid request response based on acognitive analysis of natural language artifacts. While the inventionhas been particularly shown and described in conjunction with exemplaryembodiments, it will be appreciated that variations and modificationswill occur to those skilled in the art. Therefore, it is to beunderstood that the appended claims are intended to cover all suchmodifications and changes that fall within the true spirit of theinvention.

What is claimed is:
 1. A method for optimizing resource allocation to abid request response based on a cognitive analysis of natural languageartifacts, comprising: obtaining a request and a plurality of supportingartifacts in a natural language; performing a cognitive analysis of therequest and supporting artifacts to extract a set of informationentities; normalizing the extracted information entities using alexical-relations based graph database to classify the set of extractedinformation entities as standardized concepts with which the set ofextracted information entities are most closely associated; identifying,for a portion of the request, at least a subset of the set of thestandardized concepts, with which the set of extracted informationentities are most closely associated, as a set of parameterscorresponding with a set of predetermined variables; weighting eachvariable of the set of predetermined variables according to a likelihoodthat the variable indicates a relevance of a resource to the portion ofthe request; and assigning a particular resource to the bid requestresponse in response to a probability that the particular resource isrelevant to the portion of the request based on the weighted variables.2. The method of claim 1, the method further comprising generating alist of probabilities that a set of resources are relevant to theportion of the request.
 3. The method of claim 1, the determining aprobability further comprising applying a first-order supervisedmulti-label learning classification algorithm to the weighted set ofpredetermined variables.
 4. The method of claim 1, wherein theprobability that the particular resource is relevant to the portion ofthe request has a variance and the method further comprises iteratingthe identifying, weighting, and determining until the variance is belowa pre-defined threshold.
 5. The method of claim 1, wherein the pluralityof supporting artifacts comprises an artifact selected from the groupconsisting of: historical data, public information, and correspondence.6. The method of claim 1, wherein at least one variable of the set ofpredetermined variables is a variable selected from the group consistingof: request type, deal size, techno-functional area of a deal, dealtype, deal coverage, industry, sub-domain of an industry,techno-functional area of an industry, geography, client size,historical spending, inclination to a bidder, inclination to partners,bidder strength in a domain, sales organization size of a bidder,section of the request, and techno-functional entities of the request.7. The method of claim 1, wherein the likelihood that the variableindicates a relevance of a resource to the portion of the request isbased on historical data processed by a self-learning system.
 8. Acomputer system for optimizing resource allocation to a bid requestresponse based on a cognitive analysis of natural language artifacts,the computer system comprising: a memory medium comprising programinstructions; a bus coupled to the memory medium; and a processor, forexecuting the program instructions, coupled to a bid request analysisengine via the bus that when executing the program instructions causesthe system to: obtain a request and a plurality of supporting artifactsin a natural language; perform a cognitive analysis of the request andsupporting artifacts to extract a set of information entities; normalizethe extracted information entities using a lexical-relations based graphdatabase to classify the set of extracted information entities asstandardized concepts with which the set of extracted informationentities are most closely associated; identify, for a portion of therequest, at least a subset of the set of the standardized concepts, withwhich the set of extracted information entities are most closelyassociated, as a set of parameters corresponding with a set ofpredetermined variables; weight each variable of the set ofpredetermined variables according to a likelihood that the variableindicates a relevance of a resource to the portion of the request; andassign a particular resource to the bid request response in response toa probability that the particular resource is relevant to the portion ofthe request based on the weighted variables.
 9. The computer system ofclaim 8, the instructions further causing the system to generate a listof probabilities that a set of resources are relevant to the portion ofthe request.
 10. The computer system of claim 8, the instructionsfurther causing the system to apply a first-order supervised multi-labellearning classification algorithm to the weighted set of predeterminedvariables.
 11. The computer system of claim 8, wherein the probabilitythat the particular resource is relevant to the portion of the requesthas a variance and the instructions further cause the system to iteratethe identifying, weighting, and determining until the variance is belowa pre-defined threshold.
 12. The computer system of claim 8, wherein theplurality of supporting artifacts comprises an artifact selected fromthe group consisting of: historical data, public information, andcorrespondence.
 13. The computer system of claim 8, wherein at least onevariable of the set of predetermined variables is a variable selectedfrom the group consisting of: request type, deal size, techno-functionalarea of a deal, deal type, deal coverage, industry, sub-domain of anindustry, techno-functional area of an industry, geography, client size,historical spending, inclination to a bidder, inclination to partners,bidder strength in a domain, sales organization size of a bidder,section of the request, and techno-functional entities of the request.14. The computer system of claim 8, wherein the likelihood that thevariable indicates a relevance of a resource to the portion of therequest is based on historical data processed by a self-learning system.15. A computer program product for optimizing resource allocation to abid request response based on a cognitive analysis of natural languageartifacts, the computer program product comprising a computer readablehardware storage device, and program instructions stored on the computerreadable hardware storage device, to: obtain a request and a pluralityof supporting artifacts in a natural language; perform a cognitiveanalysis of the request and supporting artifacts to extract a set ofinformation entities; normalize the extracted information entities usinga lexical-relations based graph database to classify the set ofextracted information entities as standardized concepts with which theset of extracted information entities are most closely associated;identify, for a portion of the request, at least a subset of the set ofthe standardized concepts, with which the set of extracted informationentities are most closely associated, as a set of parameterscorresponding with a set of predetermined variables; weight eachvariable of the set of predetermined variables according to a likelihoodthat the variable indicates a relevance of a resource to the portion ofthe request; and assign a particular resource to the bid requestresponse in response to a probability that the particular resource isrelevant to the portion of the request based on the weighted variables.16. The computer program product of claim 15, the computer readablestorage device further comprising instructions to generate a list ofprobabilities that a set of resources are relevant to the portion of therequest.
 17. The computer program product of claim 15, the computerreadable storage device further comprising instructions to apply afirst-order supervised multi-label learning classification algorithm tothe weighted set of predetermined variables.
 18. The computer programproduct of claim 15, wherein the probability that the particularresource is relevant to the portion of the request has a variance andthe computer readable storage device further comprising instructions toiterate the identifying, weighting, and determining until the varianceis below a pre-defined threshold.
 19. The computer program product ofclaim 15, wherein at least one variable of the set of predeterminedvariables is a variable selected from the group consisting of: requesttype, deal size, techno-functional area of a deal, deal type, dealcoverage, industry, sub-domain of an industry, techno-functional area ofan industry, geography, client size, historical spending, inclination toa bidder, inclination to partners, bidder strength in a domain, salesorganization size of a bidder, section of the request, andtechno-functional entities of the request.
 20. The computer programproduct of claim 15, wherein the likelihood that the variable indicatesa relevance of a resource to the portion of the request is based onhistorical data processed by a self-learning system.